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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
			<email_address>info@iamrp.net</email_address>
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				<full_title>Journal of Computational and Data-Driven Materials Engineering</full_title>
				<abbrev_title>J. Comput. Data-Driven Mater. Eng.</abbrev_title>
				<issn>3149-9368</issn>
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				<publication_date>
					<year>2023</year>
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					<volume>2</volume>
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				<issue>1</issue>
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				<titles>
					<title>Why Equivariant Networks Fail for Magnetic Ordering: Broken Symmetries and Hidden Degeneracies</title>
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								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Claire</given_name>
            <surname>Dupont</surname>
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            <given_name>Julien</given_name>
            <surname>Martin</surname>
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								<publication_date>
					<year>2023</year>
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          					<citation key="rk-10.68159/w194376630-a67149b4-2208-49af-9f0f-156affb46954">
					  <unstructured_citation>Batzner S, Musaelian A, Sun L, Geiger M, Mailoa JP, Kornbluth M, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun. 2022;13(1):2453.</unstructured_citation>
						 <doi>10.1038/s41467-022-29939-5</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-38b62944-d83d-45b1-af92-0b4513e8eec5">
					  <unstructured_citation>Batatia I, Kovács DP, Simm G, Ortner C, Csányi G. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Adv Neural Inf Process Syst. 2022;35:11423-36.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/w194376630-2a6d9496-ba90-42be-9991-e549c070195a">
					  <unstructured_citation>Choudhary K, DeCost B. Atomistic line graph neural network for improved materials property predictions. NPJ Comput Mater. 2021;7(1):185.</unstructured_citation>
						 <doi>10.1038/s41524-021-00650-1</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-d2633c7c-748f-40ba-b1ff-98c158a0ee0a">
					  <unstructured_citation>Horton MK, Montoya JH, Liu M, Persson KA. High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using density functional theory. NPJ Comput Mater. 2019;5(1):64.</unstructured_citation>
						 <doi>10.1038/s41524-019-0199-7</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-7a8acef1-1df6-4c6c-881a-953a0549a8bb">
					  <unstructured_citation>Frey NC, Horton MK, Munro JM, Griffin SM, Persson KA, Shenoy VB. High-throughput search for magnetic and topological order in transition metal oxides. Sci Adv. 2020;6(50):eabd1076.</unstructured_citation>
						 <doi>10.1126/sciadv.abd1076</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-c7f352c3-8c71-4dab-94b0-72b1099ce969">
					  <unstructured_citation>Chakraborty S, Gupta S, Pakhira S, Choudhary R, Biswas A, Mudryk Y, et al. Ground-state degeneracy and complex magnetism of geometrically frustrated Gd2Ir0.97Si2.97. Phys Rev B. 2022;106(22):224427.</unstructured_citation>
						 <doi>10.1103/PhysRevB.106.224427</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-dd533b5d-8b3e-41fd-952c-fbb6df29c3e5">
					  <unstructured_citation>Acosta CM, Ogoshi E, Souza JA, Dalpian GM. Machine learning study of the magnetic ordering in 2D materials. ACS Appl Mater Interfaces. 2022;14(7):9418-32.</unstructured_citation>
						 <doi>10.1021/acsami.1c21558</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-41374f41-9ec0-411d-83ed-cb2a7a08db1a">
					  <unstructured_citation>Merker HA, Heiberger H, Nguyen L, Liu T, Chen Z, Andrejevic N, et al. Machine learning magnetism classifiers from atomic coordinates. iScience. 2022;25(10):105192.</unstructured_citation>
						 <doi>10.1016/j.isci.2022.105192</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-c0606a81-9c5f-4b9a-802d-8f3367e92ad4">
					  <unstructured_citation>Katsikas G, Sarafidis C, Kioseoglou J. Machine learning in magnetic materials. Phys Status Solidi B. 2021;258(8):2000600.</unstructured_citation>
						 <doi>10.1002/pssb.202000600</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-348d156b-1c79-4bee-844f-42ef67c09eab">
					  <unstructured_citation>Jang Y, Kim CH, Go A. Classification of magnetic order from electronic structure by using machine learning. Sci Rep. 2023;13(1):12445.</unstructured_citation>
						 <doi>10.1038/s41598-023-38863-7</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-36afa1e8-d54b-4b64-8c5f-45b9e523932e">
					  <unstructured_citation>Ghosh A, Ronning F, Nakhmanson SM, Zhu JX. Machine learning study of magnetism in uranium-based compounds. Phys Rev Mater. 2020;4(6):064414.</unstructured_citation>
						 <doi>10.1103/PhysRevMaterials.4.064414</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-aa3878dd-75d5-49e5-a5d5-bc3c9ef223b7">
					  <unstructured_citation>Lu Z, Chen X, Liu X, Lin D, Wu Y, Zhang Y, et al. Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses. NPJ Comput Mater. 2020;6(1):187.</unstructured_citation>
						 <doi>10.1038/s41524-020-00460-x</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-3a29d747-0869-40e3-80c8-3895298a0a84">
					  <unstructured_citation>Court CJ, Cole JM. Magnetic and superconducting phase diagrams and transition temperatures predicted using text mining and machine learning. NPJ Comput Mater. 2020;6(1):18.</unstructured_citation>
						 <doi>10.1038/s41524-020-0287-8</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-587f2387-3078-44c9-a6bd-a9eba12c2a65">
					  <unstructured_citation>Iwasaki Y, Sawada R, Stanev V, Ishida M, Kirihara A, Omori Y, et al. Identification of advanced spin-driven thermoelectric materials via interpretable machine learning. NPJ Comput Mater. 2019;5(1):103.</unstructured_citation>
						 <doi>10.1038/s41524-019-0241-9</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-ca8a535b-803d-4503-ab93-838c9e954b0d">
					  <unstructured_citation>Iwasaki Y, Sawada R, Saitoh E, Ishida M. Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit. Commun Mater. 2021;2(1):31.</unstructured_citation>
						 <doi>10.1038/s43246-021-00135-0</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-2bb449b6-2261-4be5-948e-6c6772347a25">
					  <unstructured_citation>Kaba SO, Groleau-Paré B, Gauthier MA, Tremblay AMS, Verret S, Gauvin-Ndiaye C. Prediction of large magnetic moment materials with graph neural networks and random forests. Phys Rev Mater. 2023;7(4):044407.</unstructured_citation>
						 <doi>10.1103/PhysRevMaterials.7.044407</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-27dabeb8-1503-4982-8739-5b9b7b3d6791">
					  <unstructured_citation>Xia W, Sakurai M, Balasubramanian B, Liao T, Wang R, Zhang C, et al. Accelerating the discovery of novel magnetic materials using machine learning-guided adaptive feedback. Proc Natl Acad Sci U S A. 2022;119(47):e2204485119.</unstructured_citation>
						 <doi>10.1073/pnas.2204485119</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-c886d092-7e71-4915-ac3f-07a69e4d191f">
					  <unstructured_citation>Miyazaki Y. Equivariant neural networks for spin dynamics simulations of itinerant magnets. Mach Learn Sci Technol. 2023;4(4):045006.</unstructured_citation>
						 <doi>10.1088/2632-2153/acffa2</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-1f097f5f-61b7-4af6-bf87-cd2b7ccb58db">
					  <unstructured_citation>Li H, Wang Z, Zou N, Ye M, Xu R, Gong X, et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation. Nat Comput Sci. 2022;2(6):367-77.</unstructured_citation>
						 <doi>10.1038/s43588-022-00265-6</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-e4f6ecef-2586-4792-a1e7-c3549e3c2b7d">
					  <unstructured_citation>Bouhon A, Lange GF, Slager RJ. Topological correspondence between magnetic space group representations and subdimensions. Phys Rev B. 2021;103(24):245127.</unstructured_citation>
						 <doi>10.1103/PhysRevB.103.245127</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-d8a0e677-8ac0-4abd-9607-3ca3b5a51fe0">
					  <unstructured_citation>Watanabe H, Po HC, Vishwanath A. Structure and topology of band structures in the 1651 magnetic space groups. Sci Adv. 2018;4(8):eaat8685.</unstructured_citation>
						 <doi>10.1126/sciadv.aat8685</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-e7c0fc29-9c06-4e65-8d82-84a808fe31da">
					  <unstructured_citation>Frank G, Scherübl Z, Csonka S, Zaránd G, Pályi A. Magnetic degeneracy points in interacting two-spin systems: Geometrical patterns, topological charge distributions, and their stability. Phys Rev B. 2020;101(24):245409.</unstructured_citation>
						 <doi>10.1103/PhysRevB.101.245409</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-879c9837-3b08-40f5-833e-b500c19969d7">
					  <unstructured_citation>Nagyfalusi B, Udvardi L, Szunyogh L. Magnetic ground state of supported monatomic Fe chains from first principles. J Phys Condens Matter. 2022;34(39):395803.</unstructured_citation>
						 <doi>10.1088/1361-648X/ac8260</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-b811e151-4585-4f9d-85b6-22f0614d81bf">
					  <unstructured_citation>Yu H, Zhong Y, Ji J, Gong X, Xiang H. Time-reversal equivariant neural network potential and Hamiltonian for magnetic materials. arXiv [Preprint]. 2022.</unstructured_citation>
						 <doi>10.48550/arXiv.2211.11403</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-6dce4cc1-854c-4114-ac07-b0526ec81768">
					  <unstructured_citation>Lu S, Zhou Q, Guo Y, Wang J. On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature. Chem. 2022;8(3):769-83.</unstructured_citation>
						 <doi>10.1016/j.chempr.2021.11.009</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-b915d16e-9462-47db-82c8-f3271c264ead">
					  <unstructured_citation>Gálisová L, Kaczor M. Ground state, magnetization process and bipartite quantum entanglement of a spin-1/2 Ising-Heisenberg model on planar lattices of interconnected trigonal bipyramids. Entropy (Basel). 2021;23(12):1671.</unstructured_citation>
						 <doi>10.3390/e23121671</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-69643140-d186-4764-b660-4bf201f2d121">
					  <unstructured_citation>Nikolov S, Wood MA, Cangi A, Maillet JB, Marinica MC, Thompson AP, et al. Quantum-accurate magneto-elastic predictions with classical spin-lattice dynamics. arXiv [Preprint]. 2021.</unstructured_citation>
						 <doi>10.48550/arXiv.2101.07332</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-fc10588a-1008-4e6d-b561-6018d06929d1">
					  <unstructured_citation>Zhou X, Feng W, Yang X, Guo GY, Yao Y. Crystal chirality magneto-optical effects in collinear antiferromagnets. Phys Rev B. 2021;104(2):024401.</unstructured_citation>
						 <doi>10.1103/PhysRevB.104.024401</doi> 					</citation>
          					<citation key="rk-10.68159/w194376630-7a514888-e62e-4d71-8cb6-ff0966cbf9e4">
					  <unstructured_citation>Zheng X, Wang Y, Liu Y, Li M, Zhang M, Jin D, et al. Graph neural networks for graphs with heterophily: A survey. arXiv [Preprint]. 2022.</unstructured_citation>
						 <doi>10.48550/arXiv.2202.07082</doi> 					</citation>
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